Artificial Intelligence
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🔰 Machine Learning & Artificial Intelligence Free Resources 🔰 Learn Data Science, Deep Learning, Python with Tensorflow, Keras & many more For Promotions: @love_data
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Сеть:📈 Аналитический обзор Telegram-канала Artificial Intelligence
Канал Artificial Intelligence (@machinelearning_deeplearning) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 55 377 подписчиков, занимая 3 050 место в категории Образование и 6 211 место в регионе Индия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 55 377 подписчиков.
Согласно последним данным от 30 августа, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 683, а за последние 24 часа — 41, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 5.87%. В первые 24 часа после публикации контент обычно набирает 1.33% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 3 250 просмотров. В течение первых суток публикация набирает 736 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 25.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как learning, classification, layer, pattern, chatbot.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“🔰 Machine Learning & Artificial Intelligence Free Resources
🔰 Learn Data Science, Deep Learning, Python with Tensorflow, Keras & many more
For Promotions: @love_data”
Благодаря высокой частоте обновлений (последние данные получены 31 августа, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Образование.
55 377
Подписчики
+4124 часа
+1517 дней
+68330 день
Архив постов
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Russia is currently hosting the AI Journey international conference, during which the second season of the AI4PLANET scientific and educational video podcast was released.
The main topic of this season was the role of AI in the emergence of new professions and transformation of existing ones. The speakers of the podcast discussed in 10 episodes how AI is already helping experts and what are the prospects of using AI in the work of ecologists, climatologists, doctors, teachers, HR-specialists and security officers.
The experts sought answers to the burning questions:
▫️ How will AI strengthen the skills of the in-demand specialist of the future?
▫️ Do scientists and researchers already need to master Data Science skills now?
▫️ AI-developer for sustainable development - a new profession or a collective image of coordinated interdisciplinary work of a large team?
AI4PLANET is a technological “journey” through professions from different fields of sustainable development: from climate and ecology to psychology and professions of the future.
We invite you to visit the AI Journey international conference page and listen to the AI4PLANET video podcast.
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ARTIFICIAL INTELLIGENCE 🤖
🎥 Siraj Raval - YouTube channel with tutorials about AI.
🎥 Sentdex - YouTube channel with programming tutorials.
⏱ Two Minute Papers - Learn AI with 5-min videos.
✍️ Data Analytics - blog on Medium.
🎓 Google Machine Learning Course - A crash course on machine learning taught by Google engineers.
🌐 Google AI - Learn from ML experts at Google.
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AI/ML Roadmap👨🏻💻👾🤖 -
==== Step 1: Basics ====
📊 Learn Math (Linear Algebra, Probability).
🤔 Understand AI/ML Fundamentals (Supervised vs Unsupervised).
==== Step 2: Machine Learning ====
🔢 Clean & Visualize Data (Pandas, Matplotlib).
🏋️♂️ Learn Core Algorithms (Linear Regression, Decision Trees).
📦 Use scikit-learn to implement models.
==== Step 3: Deep Learning ====
💡 Understand Neural Networks.
🖼️ Learn TensorFlow or PyTorch.
🤖 Build small projects (Image Classifier, Chatbot).
==== Step 4: Advanced Topics ====
🌳 Study Advanced Algorithms (Random Forest, XGBoost).
🗣️ Dive into NLP or Computer Vision.
🕹️ Explore Reinforcement Learning.
==== Step 5: Build & Share ====
🎨 Create real-world projects.
🌍 Deploy with Flask, FastAPI, or Cloud Platforms.
#ai #ml
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💸 SECOND AND LAST TRADING SESSION: THE RESULTS SPEAK FOR THEMSELVES! 💸
Here is the end of the second trading session, and as always, I am thrilled with the results! 💥📈
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We were able to earn $5,900 starting from $200! Anyone could do it.
From tomorrow we start a new marathon :
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Everyone can participate absolutely free of charge! Go to the channel and start trading. Write in private messages to Lisa : “TRADING”. At the link below
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10 Things you need to become an AI/ML engineer:
1. Framing machine learning problems
2. Weak supervision and active learning
3. Processing, training, deploying, inference pipelines
4. Offline evaluation and testing in production
5. Performing error analysis. Where to work next
6. Distributed training. Data and model parallelism
7. Pruning, quantization, and knowledge distillation
8. Serving predictions. Online and batch inference
9. Monitoring models and data distribution shifts
10. Automatic retraining and evaluation of models
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Hard Pill To Swallow: 💊
Robots aren’t stealing your future - they’re taking the boring jobs.
Meanwhile:
- Some YouTuber made six figures sharing what she loves.
- A teen's random app idea just got funded.
- My friend quit banking to teach coding - he's killing it.
Here’s the thing:
Hard work still matters. But the rules of the game have changed.
The real money is in solving problems, spreading ideas, and building cool stuff.
Call it evolution. Call it disruption. Whatever.
Crying about the old world won't help you thrive in the new one.
Create something.✨
#ai
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AI Engineer
Deep Learning: Neural networks, CNNs, RNNs, transformers.
Programming: Python, TensorFlow, PyTorch, Keras.
NLP: NLTK, SpaCy, Hugging Face.
Computer Vision: OpenCV techniques.
Reinforcement Learning: RL algorithms and applications.
LLMs and Transformers: Advanced language models.
LangChain and RAG: Retrieval-augmented generation techniques.
Vector Databases: Managing embeddings and vectors.
AI Ethics: Ethical considerations and bias in AI.
R&D: Implementing AI research papers.
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What kind of problems neural nets can solve?
Neural nets are good at solving non-linear problems. Some good examples are problems that are relatively easy for humans (because of experience, intuition, understanding, etc), but difficult for traditional regression models: speech recognition, handwriting recognition, image identification, etc.
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